Tumor Time‐Course Predicts Overall Survival in Non‐Small Cell Lung Cancer Patients Treated with Atezolizumab: Dependency on Follow‐Up Time

Abstract

The large heterogeneity in response to immune checkpoint inhibitors is driving the exploration of predictive biomarkers to identify patients who will respond to such treatment. We extended our previously suggested modeling framework of atezolizumab pharmacokinetics, IL18, and tumor size (TS) dynamics, to also include overall survival (OS). Baseline and model‐derived variables were explored as predictors of OS in 88 patients with non‐small cell lung cancer treated with atezolizumab. To investigate the impact of follow‐up length on the inclusion of predictors of OS, four different censoring strategies were applied. The time‐course of TS change was the most significant predictor in all scenarios, whereas IL18 was not significant. Identified predictors of OS were similar regardless of censoring strategy, although OS was underpredicted when patients were censored 5 months after last dose. The study demonstrated that the tumor‐time course‐OS relationship could be identified based on early phase I data.

Multistate model for pharmacometric analyses of overall survival in HER2‐negative breast cancer patients treated with docetaxel

“…Various metrics derived from longitudinal models (e.g., tumor or biomarker model) have been identified as predictors of OS. 2 , 5 , 6 , 7 , 16 , 30 , 32 , 33 , 34 In contrast to traditional tumor‐OS analysis, the multistate model framework is quite flexible model for describing the hazard of death with time and the metrics are investigated as predictors of the intermediate events. The intermediate events jointly described the hazard of death.…”

“…Therefore, the existing TTE modeling approach where estimation of a single survival function to OS data has problems. The model predicted tumor size (or biomarker) is typically extrapolated until OS time during survival analysis, 5 , 6 , 7 leading to not accounting the effect of the sequential therapy. Immortal time bias originating from a failure to adequately account for time‐dependent covariates in the TTE model can be a major issue.…”

Modelling the association between biomarkers and clinical outcome: An introduction to nonlinear joint models

“…There is no closed form of the likelihood, therefore inference constitutes an important statistical challenge and requires high-performance algorithms. In order to simplify the computational complexity, alternative inferences such as two-stage 29,35,36 and sequential 37,38 approaches have been proposed, but this may lead to increased risk of bias in both longitudinal and survival parameters. 17,39 The likelihood can be approximated using quadrature such as Gauss 24 and Laplace 4,29 and then maximized using Newton-Raphson type algorithms.…”

“…In a frequentist framework, the longitudinal and survival data are independent, conditionally on the random effects, 1 and the likelihood is obtained by integrating the joint distribution over the distribution of the random effects: There is no closed form of the likelihood, therefore inference constitutes an important statistical challenge and requires high‐performance algorithms. In order to simplify the computational complexity, alternative inferences such as two‐stage 29,35,36 and sequential 37,38 approaches have been proposed, but this may lead to increased risk of bias in both longitudinal and survival parameters 17,39 …”

Artificial Intelligence and Mechanistic Modeling for Clinical Decision Making in Oncology

“…However, a subsequent study demonstrated that IL-18 had no significant impact on OS. 125 Joint models. TGI-OS models estimate the TGI and OS model parameters sequentially.…”